1. Field of the Invention
The present invention relates to an adaptive maximum likelihood sequence estimation apparatus and an adaptive maximum likelihood sequence estimation method and, more particularly, to an adaptive maximum likelihood sequence estimation apparatus which is utilized as an equalizer for compensating for a distortion occurring in a received signal during passage on a transmission path in a receiver used in digital communication, and an adaptive maximum likelihood sequence estimation method therefor.
2. Description of the Related Art
In recent years, digital mobile communication devices have been rapidly developed. Upon execution of land mobile communications, a received signal suffers a complex, considerable distortion by a multi-frequency transmission interference accompanied by a transmission delay caused by a physical environment around a mobile station, and high-speed movement of the mobile station. A mobile terminal must compensate for distortion components including noise using some signal processing means for the received signal on which noise is further superposed. The waveform equalization technique in digital mobile communications is a technique for compensating for these distortions, and two major techniques are known. One technique is a DFE (Decision Feedback Equalizer), and the other technique is an MLSE (Maximum Likelihood Sequence Estimation). The feasibility of the former technique has been examined due to easy implementation expected in terms of the calculation amount, hardware scale, and the like. The latter technique is the best one of the waveform equalization techniques, and has become implementable by the remarkable development of the LSI micropatterning techniques and the advent of high-speed digital signal processing processors (DSPs) suited for digital signal processing.
The MLSE selects a transmission signal sequence, which best matches a received signal sequence, from all transmission signal sequences, which may be transmitted, using a Viterbi algorithm. The MLSE operates under the premise that a transmission path impulse response is known by some means. Therefore, in land mobile communications such as car telephone systems, since the transmission path characteristics vary every moment, the MLSE must also change the transmission path impulse response to follow the variation. For example, when a communication is made at a transmission rate of about 20 ksymbols/s, if the speed of a mobile station has reached about 100 km/h, fading having a cycle of about a 240-symbol time occurs.
Therefore, a deep fade occurs in the received signal at a 120-symbol cycle. In order to follow such a high-speed variation transmission path, various adaptive algorithms having high-speed convergence and high-speed followability are employed in a transmission path impulse response estimation unit of the MLSE. Most of these algorithms employ an LMS algorithm due to a small calculation amount. Also, in some cases, an RLS algorithm, which has a higher convergence speed but a larger calculation amount than those of the LMS algorithm, is employed while being designed to reduce the calculation amount.
In general, in digital land mobile communications, a TDMA system is employed as the communication system, and a channel of a single frequency is time-divided into units called slots, and a plurality of users are assigned to slots to increase the subscriber capacity. In order to discriminate time-divided slots from each other or to establish the synchronization of a receiver, a known signal sequence is assigned in each slot. For example, FIG. 12 shows a slot format 92 (down link) of North American Digital Cellular defined by IS-54 of the TIA, USA. Fourteen symbols located at the head of a slot are a known signal sequence 93 (SYNC). A transmission path impulse response must be sequentially updated while a transmission path impulse response estimation initial value is calculated from this known signal sequence and a received signal sequence corresponding to the known signal sequence so as to estimate an unknown data sequence following the known signal sequence. Therefore, whether or not an estimated value goes (converges) to a true transmission response as much as possible on the basis of the known signal sequence in the short cycle determines the performance of the receiver. For this reason, employment of the above-mentioned RLS algorithm or the like with high-speed convergence performance has been examined.
The operation of the MLSE will be briefly described in detail below. As described above, the MLSE operates under the premise that the transmission path impulse response is known, and obtains only one best matching transmission signal sequence from all possible transmission signal sequences (these sequences will be referred to as candidate sequences hereinafter). In order to determine this, a likelihood is known. The likelihood in the MLSE is efficiently calculated using the Viterbi algorithm.
FIG. 13 is a state transition diagram obtained when a QPSK modulation method is employed as a modulation method. In the case of QPSK modulation, there are four different symbols which can be transmitted at one time, and these symbols will be referred to as state 0, state 1, state 2, and state 3 (99, 910, 911, 912) hereinafter. A transition route from each state at a certain time to the state at the next time will be referred to as a path (917, 918, 919, 920) hereinafter. Paying attention to state 0 at time k,
(1) there are four paths from the respective states at time k-1 to state 0 at time k. PA1 (2) There are also four paths having a history of transiting to state 0 at time k: PA1 where x.sub.j (k) means the transmission signal vector constituted by signals subjected to mapping processing in accordance with a predetermined modulation method from transmission signal candidate sequences along paths to state j (j=0 . . . , 3) at time k. PA1 (3) One and only path to each of the respective states at time k-1 is called a survivor path (913, 914, 915, 916), and transmission path impulse responses estimated by these survivor paths are respectively represented by h.sub.0 (k-1), h.sub.1 (k-1), h.sub.2 (k-1), and h.sub.3 (k-1). PA1 (4) Estimated received signals r.sub.k,0, r.sub.k,1, r.sub.k,2, and r.sub.k,3 at time k of the respective paths are calculated using the transmission path impulse responses estimated along the paths of the four candidate sequences in item (2) above. PA1 where suffix t means the transposition of the matrix. PA1 (5) A received signal at time k is represented by r.sub.k, and a square of an error between the received signal and the estimated received signal will be referred to as a branch metric (bm) hereinafter. PA1 (6) The branch metric is calculated at respective times along the survivor paths, and accumulated values of four paths to state 0 at time k are calculated. This accumulated value will be referred to as a path metric (pm) hereinafter. PA1 where j=1 , . . . , 3, and when.gtoreq.1, pm0,j=0. PA1 (7) A path (one of paths 0 to 3) having the minimum one of the four path metrics of state 0 at time k calculated in item (6) is determined as a survivor path of state 0 at time k. PA1 (8) An estimated transmission path impulse response h.sub.0 (k) along the survivor path is calculated using an adaptive algorithm on the basis of the estimated transmission signal sequence of a finite length along the survivor path to state 0 at time k and the actual received signal sequence. PA1 (9) This operation is similarly repeated from state 0 to state 3 at time k, and survivor paths to all the states at time k and estimated transmission path impulse responses are determined. PA1 (10) When the last symbol of a sequence to be processed has been reached, a sequence with a minimum path metric calculated along a survivor path is determined as the final estimated transmission signal sequence. PA1 first estimation means for estimating a transmission signal sequence from received signals on the basis of an estimated transmission path impulse response; PA1 second estimation means for estimating an estimated received signal at time k on the basis of one of a known signal sequence and the transmission signal sequence estimated by the first estimation means, and a transmission path impulse response estimated at time k-1; PA1 error signal generation means for generating an error signal on the basis of a received signal at time k and the estimated received signal at time k from the second estimation means; and PA1 third estimation means for estimating a transmission path impulse response at time k using a predetermined adaptive algorithm on the basis of the error signal generated by the error signal generation means, PA1 wherein the third estimation means comprises means for estimating a transmission path impulse response by a non-recursive calculation during a reception period of a known signal sequence of the received signal, and for estimating a transmission path impulse response by a recursive calculation during a reception period of an unknown data signal sequence of the received signals following the known signal sequence period. PA1 the first estimation step of estimating a transmission signal sequence from a received signal on the basis of an estimated transmission path impulse response; PA1 the second estimation step of estimating an estimated received signal at time k on the basis of one of a known signal sequence and the transmission signal sequence estimated in the first estimation step, and a transmission path impulse response estimated at time k-1; PA1 the error signal generation step of generating an error signal on the basis of a received signal at time k and the estimated received signal at time k; and PA1 the third estimation step of estimating a transmission path impulse response at time k using a predetermined adaptive algorithm on the basis of the error signal generated in the error signal generation step, PA1 wherein the third estimation step comprises the step of estimating a transmission path impulse response by a non-recursive calculation during a reception period of a known signal sequence of the received signal, and of estimating a transmission path impulse response by a recursive calculation during a reception period of an unknown data signal sequence of the received signals following the known signal sequence period.
time i k k-1 k-2 k-3 k-4 PA2 path 0.fwdarw.0-0-2-3-1 . . . .fwdarw.x.sub.0 (k) PA2 path 1.fwdarw.0-1-3-3-1 . . . .fwdarw.x.sub.1 (k) PA2 path 2.fwdarw.0-2-1-2-1 . . . .fwdarw.x.sub.2 (k) PA2 path 3.fwdarw.0-3-0-2-1 . . . .fwdarw.x.sub.3 (k) PA2 r.sub.k,j =x.sub.j.sup.t (k)h.sub.j (k-1).fwdarw.estimated received signal of path j at time k PA2 bm.sub.k,j =.vertline.r.sub.k -r.sub.k,j .vertline..sup.2 .fwdarw.branch metric of path j at time k PA2 pm.sub.k,0,j =pm.sub.k-1,j +bm.sub.k,j
The adaptive algorithm used in transmission path impulse response estimation of the MLSE will be briefly described below. Upon estimation of the transmission path impulse response along a survivor path in the above-mentioned item (8), the LMS algorithm or RLS algorithm to be described below is often used. A transmission signal vector constituted by a sequence which is mapped in accordance with a predetermined modulation method from a transmission signal candidate sequence along a survivor path in an arbitrary state at time k is represented by x(k)=[x.sub.k-1, . . . , x.sub.k-L+1 ].sup.t. Suffix t means the transposition of the matrix. L is the tap length of a transversal filter which simulates the transmission path impulse response. At time k, an estimated received signal r.sub.k is calculated based on a transmission path impulse response h(k-1) at time k-1. A difference, called an error signal e.sub.k, between the estimated received signal and an actual received signal r.sub.k is given by: EQU e.sub.k =r.sub.k -r.sub.k =r.sub.k -x.sup.t (k)h(k-1)
In the LMS algorithm, the estimated transmission path impulse response h(k) at time k is calculated as follows: EQU h(k)=h(k-1)+.mu.e.sub.k x*(k).fwdarw.LMS algorithm (a)
where .mu. (0&lt;.mu.&lt;1) is the step size, and suffix * means the conjugate of the matrix.
In the RLS algorithm, h(k) is calculated as follows. The error signal e.sub.k is the same as that described above. EQU K(k)=P(k-1)x*(k)/{.omega.+x.sup.t (k)P(k-1)x*(k)} EQU P(k)={P(k-1)-K(k)x.sup.t (k)P(k-1)}/.omega. EQU h(k)=h(k-1)+K(k)e.sub.k .fwdarw.RLS algorithm (b)
where K(k) is the Kalman gain vector, P(k) is the covariance matrix of the error signal e.sub.k at time k, and .omega. is the forgetting coefficient.
As can be understood from the above description, since the RLS algorithm requires a larger amount of complicated matrix calculations than the LMS algorithm does, the total calculation amount becomes huge, and the RLS algorithm is rarely employed in the conventional system. Since K(k) is known during a known signal period, an MLSE, which comprises a transmission path impulse response estimation unit having the same high-speed convergence performance as the RLS algorithm while having the same calculation amount as the LMS algorithm by using K(k) in place of .mu.x*(k) of the LMS algorithm, is also available.
However, the above-mentioned two adaptive algorithms, i.e., the LMS and RLS algorithms, require a certain known signal sequence length and repetitive calculations until they reach an optimal solution since they are recursive updating type algorithms, although they are adaptive algorithms having high-speed convergence performance. The known signal sequence length until convergence depends on the step size in the LMS algorithm, and depends on the forgetting coefficient in the RLS algorithm. However, the known signal sequence length of 14 symbols in the above-mentioned North American format slot shown in FIG. 12 is insufficient. In the MLSE, sequence estimation performance (code error rate) of an unknown signal sequence (information data) received after the known signal sequence largely depends on initial transmission path impulse response estimation including the influence of additional noise with high accuracy during the known signal sequence period when the viterbi algorithm is started before the influence of the additional noise is sufficiently suppressed and the estimated value converges to a true transmission path impulse response, a maximum likelihood estimation error may occur.
In either of the above-mentioned adaptive algorithms, i.e., the LMS and RLS algorithms, an operation for executing calculation processing using the above-mentioned equation (a) or (b) at each time to sequentially converge a solution to an optimal solution is required during the known signal sequence period.
Furthermore, in the conventional sequential updating MLSE, the tap length of a transversal filter which simulates the transmission path impulse response is fixed, and is normally determined based on the maximum value of the multi-path delay time of the transmission path. However, when the multi-path delay amount does not correspond to an integer multiple of an information transmission cycle, the intersymbol interference amount in a received signal increases, and the number of taps determined by the maximum value of the multi-path delay amount of the transmission path is normally short. An optimal number of taps changes in correspondence with the multi-path delay amount.